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Advanced Oversampling for Improved Detection of Software Anomalies in a Robot

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15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020) (SOCO 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1268))

Abstract

Anomaly detection has been a challenging topic for decades and it still is open to new contributions nowadays. More specifically, the detection of anomalies (not only hardware ones but also those affecting the software) suffers from many problems when monitoring cyber-physical systems. One such usual problem is the much fewer data samples of anomalies than those available for the normal functioning of systems. This class-imbalance problem is addressed in the present paper and a novel strategy for oversampling the minority class is applied to an open dataset containing information about the performance of a component-based robot. The proposed strategy mainly consists on selecting the instances to be oversampled according to different criteria instead of randomly oversampling. Obtained results demonstrate that the proposed strategy improves predictive performance, especially when the SVM (Support Vector Machine) is used as classifier.

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Authors and Affiliations

  1. Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Av. Cantabria s/n, 09006, Burgos, Spain

    Nuño Basurto, Carlos Cambra & Álvaro Herrero

  2. Department of Systems and Computer Networks, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Michał  Woźniak

Authors
  1. Nuño Basurto
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  2. Michał  Woźniak
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  3. Carlos Cambra
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  4. Álvaro Herrero
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Corresponding author

Correspondence to Álvaro Herrero .

Editor information

Editors and Affiliations

  1. Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Burgos, Spain

    Álvaro Herrero

  2. Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Burgos, Spain

    Carlos Cambra

  3. Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Burgos, Spain

    Daniel Urda

  4. Technological Institute of Castilla y León, Burgos, Spain

    Javier Sedano

  5. Department of Industrial Engineering, University of A Coruña, La Coruña, Spain

    Héctor Quintián

  6. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Basurto, N., Woźniak, M., Cambra, C., Herrero, Á. (2021). Advanced Oversampling for Improved Detection of Software Anomalies in a Robot. In: Herrero, Á., Cambra, C., Urda, D., Sedano, J., Quintián, H., Corchado, E. (eds) 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020). SOCO 2020. Advances in Intelligent Systems and Computing, vol 1268. Springer, Cham. https://doi.org/10.1007/978-3-030-57802-2_1

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